The goal of this Mentored Patient-Oriented Research Career Development Award (K23) is to allow Dr. Ragg to become an independent, transdisciplinary researcher with the distinctive qualifications to integrate computational methods and high throughput molecular biology methods into clinical trials. This application includes both a systematic career development plan and a clinical research project. The career development plan includes: I) formal didactic training through participation in the IU Clinical Investigator Training Enhancement Program; and 2) careful mentorship by Munro Peacock, M.D., Clement McDonald, M.D., and Janet Hock B.D.S., Ph.D. as Dr. Ragg progresses into a truly independent clinical investigator. The patient-oriented research proposal describes the different approaches that can be taken to improve patient survival in osteosarcoma: clinical, molecular, and computational. The clinical strategy seeks to find a blood screening test to predict at the time of diagnosis which patients will fail standard chemotherapy. Our hypothesis is that osteosarcoma patients exhibit distinctive sets of circulating proteins (biomarkers) that predict the response to chemotherapy. This will be tested by the following specific aims. We will first establish the distinctive serum protein profiles of patients with osteosarcoma and identify the serum protein profiles predictive of chemotherapy responsiveness. These biomarkers will then be tested in a prospective clinical trial. To be able to carry out this research, we will establish a high-quality serum bank from pediatric patients at the General Clinical Research Center. The molecular approach seeks to find target genes that can be modified. The most promising is a DNA helicase, RECQL4. Our hypothesis is that this helicase is frequently mutated in osteosarcoma and we will determine the frequency of RECQL4 mutations in sporadic osteosarcomas. The computational approach seeks to integrate all the available clinical and laboratory research in osteosarcoma into a multidisciplinary, interactive data management system that will allow investigators to identify new associations, correlations, and trends through mathematical modeling, data mining, and visual exploration of the available data. We will build a database system, with the support of the investigators from the bone tumor committee of the Children's Oncology Group that will ultimately contain all the data relevant to osteosarcoma research.